There are recognized benefits in having systems and methods to monitor the operation of vehicles, for capturing real-time data pertaining to driving activity and patterns thereof. Such systems and methods facilitate the collection of qualitative and quantitative information related to the contributing causes of vehicle incidents, such as accidents; and allow objective driver evaluation to determine the quality of driving practices. The potential benefits include preventing or reducing vehicle accidents and vehicle abuse; and reducing vehicle operating, maintenance, and replacement costs. The social value of such devices and systems is universal, in reducing the impact of vehicle accidents. The economic value is especially significant for commercial and institutional vehicle fleets, as well as for general insurance and risk management.
There exists a large and growing market for vehicle monitoring systems that take advantage of new technological advances. These systems vary in features and functionality and exhibit considerable scope in their approach to the overall problem. Some focus on location and logistics, others on engine diagnostics and fuel consumption, whereas others concentrate on safety management.
For example, U.S. Pat. No. 4,500,868 to Tokitsu et al. (herein denoted as “Tokitsu) is intended as an adjunct in driving instruction. By monitoring a variety of sensors (such as engine speed, vehicle velocity, selected transmission gear, and so forth), a system according to Tokitsu is able to determine if certain predetermined condition thresholds are exceeded, and, if so, to signal an alarm to alert the driver. Alarms are also recorded for later review and analysis. In some cases, a simple system such as Tokitsu can be valuable. For example, if the driver were to strongly depress the accelerator pedal, the resulting acceleration could exceed a predetermined threshold and sound an alarm, cautioning the driver to reduce the acceleration. If the driver were prone to such behavior, the records created by Tokitsu's system would indicate this. On the other hand, Tokitsu's system is of limited value under other conditions. For example, if the driver were to suddenly apply the vehicle brakes with great force, the resulting deceleration could exceed a predetermined threshold, and thereby signal an alarm and be recorded. Although the records of such behavior could be valuable, such strong braking is usually done under emergency conditions where the driver is already aware of the emergency, and where an alarm would be superfluous (and hence of little or no value), or perhaps distracting (and hence of dubious value or even detrimental).
U.S. Pat. No. 4,671,111 to Lemelson (herein denoted as “Lemelson 111”) teaches the use of accelerometers and data recording/transmitting equipment for obtaining and analyzing vehicle acceleration and deceleration. Although Lemelson 111 presents this in the context of analyzing vehicle performance, however, there is no detailed discussion of precisely how an analysis of the resulting data would be done, nor how meaningful information could be obtained thereby. In related U.S. Pat. No. 5,570,087 also to Lemelson (herein denoted as “Lemelson 087”) the analyzed vehicular motion is expressed in coded representations which are stored in computer memory. As with Lemelson 111, which does not describe how raw data is analyzed to determine driving behavior parameters, Lemelson 087 does not describe how coded representations of raw data or driving behavior parameters would be created or utilized. It is further noted that U.S. Pat. No. 5,805,079 to Lemelson (herein denoted as “Lemelson 079”) is a continuation of Lemelson 087 and contains no new or additional descriptive material.
U.S. Pat. No. 5,270,708 to Kamishima (herein denoted as “Kamishima”) discloses a system that detects a vehicle's position and orientation, turning, and speed, and coupled with a database of past accidents at the present location, determines whether the present vehicle's driving conditions are similar to those of a past accident, and if so, alerts the driver. If, for example, the current vehicle speed on a particular road exceeds the (stored) speed limit at that point of the road, the driver could be alerted. Moreover, if excessive speed on that particular area is known to have been responsible for many accidents, the system could notify the driver of this. The usefulness of such a system, however, depends critically on having a base of previous data and being able to associate the present driving conditions with the stored information. The Kamishima system, in particular, does not analyze driving behavior in general, nor draw any general conclusions about the driver's patterns in a location-independent manner.
U.S. Pat. No. 5,546,305 to Kondo (herein denoted as “Kondo”) performs an analysis on raw vehicle speed and acceleration, engine rotation, and braking data by time-differentiating the raw data and applying threshold tests. Although such an analysis can often distinguish between good driving behavior and erratic or dangerous driving behavior (via a driving “roughness” analysis), time-differentiation and threshold detection cannot by itself classify raw data streams into the familiar patterns that are normally associated with driving. Providing a count of the number of times a driver exceeded a speed threshold, for example, may be indicative of unsafe driving, but such a count results in only a vague sense of the driver's patterns. On the other hand, a context-sensitive report that indicates the driver repeatedly applies the brake during turns would be far more revealing of a potentially-dangerous driving pattern. Unfortunately, however, the analysis performed by Kondo, which is typical of the prior art analysis techniques, is incapable of providing such context-sensitive information. (See “Limitations of the Prior Art” below.)
U.S. Pat. No. 6,060,989 to Gehlot (herein denoted as “Gehlot”) describes a system of sensors within a vehicle for determining physical impairments that would interfere with a driver's ability to safely control a vehicle. Specific physical impairments illustrated include intoxication, fatigue and drowsiness, or medicinal side-effects. In Gehlot's system, sensors monitor the person of the driver directly, rather than the vehicle. Although this is a useful approach in the case of physical impairments (such as those listed above), Gehlot's system is ineffective in the case of a driver who is simply unskilled or who is driving recklessly, and is moreover incapable of evaluating a driver's normal driving patterns.
U.S. Pat. No. 6,438,472 to Tano, et al. (herein denoted as “Tano”) describes a system which analyzes raw driving data (such as speed and acceleration data) in a statistical fashion to obtain statistical aggregates that can be used to evaluate driver performance. Unsatisfactory driver behavior is determined when certain predefined threshold values are exceeded. A driver whose behavior exceeds a statistical threshold from what is considered “safe” driving, can be deemed a “dangerous” driver. Thresholds can be applied to various statistical measures, such as standard deviation. Because Tano relies on statistical aggregates and thresholds which are acknowledged to vary according to road location and characteristics, however, a system according to Tano has limited ability to evaluate driver performance independent of the statistical profiles and thresholds. In particular, the statistical characterization of a driver's performance is generally not expressible in terms of familiar driving patterns. For example, a driver may have a statistical profile that exceeds a particular lateral acceleration threshold, and the driver may therefore be classified as a “dangerous” driver. But what driving pattern is responsible for excessive lateral acceleration? Is it because this driver tends to take curves too fast? Or is it because he tends to change lanes rapidly while weaving in and out of traffic? Both are possibly “dangerous” patterns, but a purely threshold-oriented statistical analysis, such as presented in Tano, may be incapable of discriminating between these, and therefore cannot attribute the resulting statistical profile to specific patterns of driving. As noted for Kondo's analysis (above), Tano's statistical analysis is also incapable of providing information in terms of familiar driving patterns.
In addition to the above issued patents, there are several commercial products currently available for monitoring vehicle driving behavior. The “Mastertrak” system by Vetronix Corporation of Santa Barbara, Calif. is intended as a fleet management system which provides an optional “safety module”. This feature, however, addresses only vehicle speed and safety belt use, and is not capable of analyzing driver behavior patterns. The system manufactured by SmartDriver of Houston, Tex. monitors vehicle speed, accelerator throttle position, engine RPM, and can detect, count, and report on the exceeding of thresholds for these variables. Unfortunately, however, there are various driving patterns which cannot be classified on the basis of thresholds, and which are nevertheless pertinent to detecting questionable or unsafe driving behavior. For example, it is generally acknowledged that driving too slowly on certain roads can be hazardous, and for this reason there are often minimum speed limits. Driving below a minimum speed, however, is not readily detectable by a system such as SmartDriver, because introducing a low-speed threshold results in such a large number of false reports (when the vehicle is driven slowly in an appropriate location) that collecting such data is not normally meaningful.
Limitations of the Prior Art
Collecting raw physical data on vehicle operation through a multiplicity of sensors usually results in a very large quantity of data which is cumbersome to store and handle, and impractical to analyze and evaluate. For this reason, any automated system or method of driver behavior analysis and evaluation must employ some abstraction mechanism to reduce the data to a manageable size and in a meaningful way.
For the prior art, as exemplified by the specific instances cited above, this is done through statistical processing and the use of predetermined thresholds, supplemented in some cases by limited continuous pre-processing (e.g., time-differentiation), optionally correlated in some cases with available history or other data on the location where the driving is being done. As a result, prior art systems and methods are generally limited to providing aggregate and statistically-processed overviews of driver performance. This is expressed succinctly in Lemelson 111: “The computer analysis may determine the manner in which the vehicle is driven, either during a specific time interval or a number of time intervals or over a longer period of time wherein averaging is employed to determine the general performance or use of the vehicle”(column 1 lines 21-26). That is, prior art analysis and evaluation is based on overall performance during a particular driving session, or is based on statistical averages over a number of different sessions. In limited cases, the analysis and evaluation can be made with regard to a particular road or road segment, through the application of GPS locating.
FIG. 1 illustrates the general prior art analysis and evaluation approach. A typical set of sensors 101 has a tachometer 103, a speedometer 105, one or more accelerometers 107, a GPS receiver 109, and optional additional sensors 111. In the case of accelerometers, it is understood that an accelerometer is typically operative to monitoring the acceleration along one particular specified vehicle axis, and outputs a raw data stream corresponding to the vehicle's acceleration along that axis. Typically, the two main axes of vehicle acceleration that are of interest are the longitudinal vehicle axis—the axis substantially in the direction of the vehicle's principal motion (“forward” and “reverse”); and the transverse (lateral) vehicle axis—the substantially horizontal axis substantially orthogonal to the vehicle's principal motion (“side-to-side”). An accelerometer which is capable of monitoring multiple independent vector accelerations along more than a single axis (a “multi-axis” accelerometer) is herein considered as, and is denoted as, a plurality of accelerometers, wherein each accelerometer of the plurality is capable of monitoring the acceleration along only a single axis. Additional sensors can include sensors for driver braking pressure, accelerator pressure, steering wheel control, handbrake, turn signals, and transmission or gearbox control, clutch (if any), and the like. Some of the sensors, such as tachometer 103 and speedometer 105 may simply have an analog signal output which represents the magnitude of the quantity. Other sensors, such as a transmission or gearbox control sensor may have a digital output which indicates which gear has been selected. More complex output would come from GPS receiver 109, according to the formatting standards of the manufacturer or industry. Other sensors can include a real-time clock, a directional device such as a compass, one or more inclinometers, temperature sensors, precipitation sensors, available light sensors, and so forth, to gauge actual road conditions and other driving factors. Digital sensor output is also possible, where supported. The output of sensor set 101 is a stream of raw data, in analog and/or digital form.
Sensor outputs are input into an analysis and evaluation unit 113, which has threshold settings 115 and a threshold discriminator 117. A statistical unit 119 provides report summaries, and an optional continuous processing unit 121 may be included to preprocess the raw data. The output of analysis and evaluation unit 113 is statistically-processed data.
A report/notification/alarm 123 is output with the results of the statistical analysis, and may contain analysis and evaluations of one or more of the following: an emergency alert 125, a driving session 1 statistics report 127, a driving session 2 statistics report 129, etc., and a driving session n statistics report 131, a driving session average statistics report 133, and a road-specific driving session statistics report 135.
These reports may be useful in analyzing and evaluating driver behavior, skill, and attitude, but the use of statistics based predominantly on thresholds or on localization of the driving, and the aggregation over entire driving sessions or groups of driving sessions also result in the loss of much meaningful information.
In particular, the details of the driver's behavior in specific driving situations are not available. Familiar driving situations, such as passing, lane changing, traffic blending, making turns, handling intersections, handling off- and on-ramps, driving in heavy stop- and-go traffic, and so forth, introduce important driving considerations. It is evident that the aggregate statistics for a given driver in a given driving session depend critically on the distribution and mix of these situations during that given session.
For example, the same driver, driving in a consistent manner but handling different driving situations may exhibit completely different driving statistics. One of the key benefits of monitoring driving behavior is the ability to determine a driver's consistency, because this is an important indicator of that driver's predictability, and therefore of the safety of that driver's performance. If the driver begins to deviate significantly from an established driving profile, this can be a valuable advance warning of an unsafe condition. Perhaps the driver is fatigued, distracted, or upset, and thereby poses a hazard which consistency analysis can detect. It is also possible that the driver has been misidentified and is not the person thought to be driving the vehicle. Unfortunately, however, statistically aggregating data, as is done in the prior art, does not permit a meaningful consistency analysis, because such an analysis depends on the particular driving situations which are encountered, and prior art analysis completely ignores the specifics of those driving situations.
A typical prior art report presents information such as: the number of times a set speed limit was exceeded; the maximum speed; the number of times a set RPM limit was exceeded; the maximum lateral acceleration or braking deceleration; and so forth. Such information may be characteristic of the driver's habits, but it would be much better to have a report that is based on familiar driving situations, maneuvers, and patterns—for example, by revealing that the driver has a habit of accelerating during turns, or makes frequent and rapid high-speed lane changes.
As another example of the limitations of the prior art, a new and relatively inexperienced driver might drive very cautiously and thereby have very “safe” overall statistics, but might lack skills for handling certain common but more challenging driving situations. An experienced driver, however, might exhibit what appear to be more “dangerous” overall statistics, but might be able to handle those challenging driving situations much better and more safely than the new driver. Prior art analysis systems and methods, however, might erroneously deduce that the more experienced driver poses the greater hazard, whereas in reality it is the apparently “safer” driver who should be scrutinized more carefully.
There is thus a need for, and it would be highly advantageous to have, a method and system for analyzing a raw vehicle data stream to determine the corresponding sequence of behavior and characteristics of the vehicle's driver in the context of familiar driving situations, and for expressing driving behavior and characteristics in terms of familiar driving patterns and maneuvers. This goal is met by the present invention.